De Moivre–Laplace theorem

De Moivre–Laplace theorem

In probability theory, the de Moivre–Laplace theorem is a normal approximation to the binomial distribution. It is a special case of the central limit theorem. It states that the binomial distribution of the number of "successes" in "n" independent Bernoulli trials with probability "p" of success on each trial is approximately a normal distribution with mean "np" and standard deviation sqrt{npq}, if "n" is very large and some conditions are satisfied.

The theorem first appeared in "The Doctrine of Chances" by Abraham de Moivre, published in 1733. The "Bernoulli trials" were not so-called in that book, but rather de Moivre wrote about the probability distribution of the number of times "heads" appears when a coin is tossed 1800 times.Fact|date=September 2008

The theorem

If n ightarrow infty, then for "k" in the sqrt{npq}-neighborhood of "np", we can approximate [Papoulis, Pillai, "Probability, Random Variables, and Stochastic Processes", 4th Edition]

:left( egin{array}{c} n \ k end{array} ight) p^k q^{n-k} simeq frac{1}{sqrt{2 pi npqe^{-(k-np)^2 / 2npq}, p+q=1, p>0, q>0.

The limiting form of theorem states that

:frac{sqrt{2 pi npq} left( egin{array}{c} n \ k end{array} ight) p^k q^{n-k{e^{-(k-np)^2 / 2npq ightarrow 1

as n ightarrow infty.

References


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